Field of the Invention
The present invention concerns the field of segmenting an organ or other region of interest in an image, or image data, acquired in a magnetic resonance data acquisition scan procedure, and in particular to implementing such segmentation using an atlas or a model that does not precisely cover or encompass the organ or region of interest that is to be segmented.
Description of the Prior Art
Magnetic resonance imaging is a widely used imaging modality for providing images of a patient to a physician in order to allow the physician to make a particular medical diagnosis. A general explanation of the fundamental concepts of magnetic resonance imaging is as follows.
Magnetic resonance data are acquired from a patient by placing the patient on a movable bed or support within an opening of the magnetic resonance scanner that is designed to receive the patient therein. A basic field magnet of the magnetic resonance scanner generates a strong and highly uniform basic magnetic field that causes nuclear spins within the patient to be aligned according to the field lines of the basic magnetic field. One or more radio-frequency (RF) pulses are then radiated by an RF radiator in the magnetic resonance scanner and the RF energy in the RF field associated with such RF pulses causes certain nuclear spins to be tilted from the alignment produced by the basic magnetic field, by an amount that is commonly called the flip angle. After the RF pulse terminates, these “flipped” nuclear spins begin to relax and, in doing so, emit RF signals that are referred to as magnetic resonance signals.
As the nuclear spins emit these magnetic resonance signals, gradient pulses are activated that spatially encode the magnetic resonance signals, with readout of the magnetic resonance signals taking place during the activation of a gradient pulse also known as a readout pulse.
The relaxation or decay of the excited nuclear spins following termination of the RF pulse is called an echo, and the duration of this echo is called the echo time, designated TE. The acquired magnetic resonance signals are analog signals, and these analog signals are supplied to a computer wherein they are digitized and converted into complex numbers, and are entered into an electronic memory. These values in the electronic memory represent a mathematical domain known as k-space, and thus the values in the memory are referred to as k-space data, or raw data.
The raw data must then be converted into image data so as to be viewable as a magnetic resonance image of the subject, or at least an image of the region of the subject from which the raw data were acquired. Many image reconstruction algorithms are known for reconstructing an image of the patient from the raw data.
The image data are made available from the computer that executed the reconstruction algorithm as a data file, which can be provided to a display monitor for viewing, or can be archived for storage and later viewing, or can be electronically transferred to any remote location for viewing or storage at the remote location. The basic operation of such a magnetic resonance tomography apparatus, and the magnetic resonance scanner thereof, are well known to those of ordinary skill in the field of magnetic resonance imaging, and need not be described in more detail herein.
For use in all types of image processing, including medical image processing, segmentation techniques or algorithms are known for extracting a desired item from a larger image in which the desired item is represented. In the case of medical imaging, the extracted item is usually the respective organ for which the diagnosis is to be made.
In the case of magnetic resonance images in which bones are represented, bone segmentation presents a challenge, for several reasons. Due to the short relaxation times (echo times) of the magnetic resonance signals produced by nuclei in bone tissue, the magnetic resonance signal, particularly in cortical bone, is low, when the magnetic resonance scanner is operated according to a conventional sequence of RF pulses and gradients. This makes it difficult, or almost impossible, to distinguish between bone and air or lung tissue, for example. Special data acquisition sequences, such as those that produce an ultra-short TE, and which are thus capable of acquiring signals from water-bound protons within the inorganic cortical structure of the bone, usually suffer with regard to image quality or other restrictions that limit the practical use of such sequences. Moreover, such special sequences often are not suitable for diagnostic purposes, thereby disqualifying their use in clinical routine.
Despite these problems, bone segmentation is an important consideration for many magnetic resonance applications, such as for attenuation correction in MR-PET, and dose calculation in MR-based radio therapy planning. Applications such as MR bone scans also benefit from advanced visualizations, such as cropping the content of bone or planar projections of bone structures, which is possible if anatomical bone information is available. Moreover, quantitative results, such as changes in MR biomarkers, such as ADC values in the bone marrow can be automatically derived, if information about the bone structure is available.
The current state of the art for bone segmentation is to start with a data file frim a model library (memory) or an atlas, which make use of a common diagnostic MR imaging contrast (Dixon technique) to non-rigidly register an articulated model of several bones of known shapes, in order to segment the bone in an unknown example from an acquired image. This technique is described in United States Patent Application Publication No. 2015/0023575 A1, the content of which is incorporated herein by reference.
A drawback of this known technique is that, for each bone, high-quality co-registered pairs of MR and CT scans are required, which serve as the model. For fine bones, such as in the shoulder or the ribs, it is difficult to meet this requirement. This means that current implementation of this known method is not suitable for all bones. Moreover, anatomical variants beyond the limits of the non-rigid registration can lead to inaccurate segmentation results.
Other methods have been proposed to segment bone directly from diagnostic MR contrasts in acquired MR data. One such method is a patch-based segmentation from multiple MR contrasts, using a deep-learning approach, which is described in “Patch-Based Generation of a Psuedo CT From Conventional MRI Sequences For MRI-Only Radio Therapy of the Brain,” Andreasen et al., Medical Physics, Vol. 42(4), pp 1596-1605 (2015). A drawback of this known technique is that, due to the bias-variance tradeoff, the training data may not include all possible variants of MR contrast, which results in a strong sensitivity to the input contrasts, which can be a problem if, in practice, the acquisition protocols are modified, or studies are done using different hardware settings, and appropriate normalization algorithms are not available.